HEAD <<<<<<< HEAD ======= >>>>>>> main <<<<<<< HEAD ======= <<<<<<< HEAD >>>>>>> main ======= >>>>>>> Added the map of beer consumption
The report will be collating specific sections of this data pertaining to countrywise policy responses, consumption pattern, youth and alcohol, levels of consumption and harms and consequences.
This report is being created using the data from the GISAH (Global Information System on Alcohol and Health) tool of the World Health Organisation. GISAH tool helps monitor trends relating to alcohol consumption across various age groups, side effects (harm caused) as well as the policy responses by different countries.
Team Name: Hayaku
Researchers:
consumption <- read_csv("../clean_data/consumption_join.csv")# Origin data pivot longer
b_tidy <- consumption %>%
filter(Beverage_Types == "Spirits") %>%
pivot_longer(cols = -c(Country, Beverage_Types),
names_to = "Year",
values_to = "Litres")
b_tidy# A tibble: 11,210 x 4
Country Beverage_Types Year Litres
<chr> <chr> <chr> <dbl>
1 Afghanistan Spirits 2018 0.01
2 Afghanistan Spirits 2017 0.01
3 Afghanistan Spirits 2016 0.01
4 Afghanistan Spirits 2015 0
5 Afghanistan Spirits 2014 0.01
6 Afghanistan Spirits 2013 0
7 Afghanistan Spirits 2012 0
8 Afghanistan Spirits 2011 0
9 Afghanistan Spirits 2010 0
10 Afghanistan Spirits 2009 0
# … with 11,200 more rows
# Loading countries data
countries <- ne_countries(returnclass = "sf", scale = "medium") %>%
select(admin, continent, pop_est, gdp_md_est, economy, income_grp )# Test unmatched countries
anti_join(b_tidy, countries, by = c("Country" = "admin"))# A tibble: 1,416 x 4
Country Beverage_Types Year Litres
<chr> <chr> <chr> <dbl>
1 Bahamas Spirits 2018 3.78
2 Bahamas Spirits 2017 3.74
3 Bahamas Spirits 2016 3.6
4 Bahamas Spirits 2015 3.61
5 Bahamas Spirits 2014 3.54
6 Bahamas Spirits 2013 3.46
7 Bahamas Spirits 2012 3.56
8 Bahamas Spirits 2011 3.68
9 Bahamas Spirits 2010 3.69
10 Bahamas Spirits 2009 3.69
# … with 1,406 more rows
# Modify the country name consisting with rnaturalearth package
b_l_country <- b_tidy %>%
mutate(Country = recode(Country,
"Bahamas" = "The Bahamas",
"Bolivia (Plurinational State of)" = "Bolivia",
"Brunei Darussalam" = "Brunei",
"Cabo Verde" = "Cape Verde",
"Congo" = "Democratic Republic of the Congo",
"Côte d'Ivoire" = "Ivory Coast",
"Czechia" = "Czech Republic",
"Democratic People's Republic of Korea" = "North Korea",
"Eswatini" = "Swaziland",
"Guinea-Bissau" = "Guinea Bissau",
"Iran (Islamic Republic of)" = "Iran",
"Lao People's Democratic Republic" = "Laos",
"Micronesia (Federated States of)" = "Federated States of Micronesia",
"North Macedonia" = "Macedonia",
"Republic of Korea" = "South Korea",
"Republic of Moldova" = "Moldova",
"Russian Federation" = "Russia",
"Serbia" = "Republic of Serbia",
"Syrian Arab Republic" = "Syria",
"Timor-Leste" = "East Timor",
"Tuvalu" = "Wallis and Futuna",
"United Kingdom of Great Britain and Northern Ireland" = "United Kingdom",
"Venezuela (Bolivarian Republic of)" = "Venezuela",
"Viet Nam" = "Vietnam"))%>%
filter(!is.na(Country))# Beer popularity by time
b_l_country_time <- left_join(countries, b_l_country, by = c("admin" = "Country"))
b_time <- ggplot()+
geom_sf(data = countries, fill = NA)+
geom_sf(data = b_l_country_time %>% filter(!is.na(Year)), mapping = aes(fill = Litres))+
scale_fill_viridis(na.value="white")+
labs(title = "Countries where Beer is popular",
subtitle = "Year: {current_frame}",
fill = "Total")+
theme_bw()+
transition_manual(Year)
animate(b_time, duration = 30, fps = 20, width = 1000, height = 500, renderer = gifski_renderer())pie
consumption %>%
select(Beverage_Types, "2018") %>%
group_by(Beverage_Types) %>%
summarise(no = sum(`2018`, na.rm = TRUE))# A tibble: 5 x 2
Beverage_Types no
<chr> <dbl>
1 All types 902.
2 Beer 372.
3 Other alcoholic beverages 97.6
4 Spirits 265.
5 Wine 174.
# The number of litres consumed for each country
b_l_country <- b_tidy %>%
group_by(Country)%>%
summarise(Litres = sum(Litres, na.rm = TRUE))
b_l_country# A tibble: 189 x 2
Country Litres
<chr> <dbl>
1 Afghanistan 0.08
2 Albania 66.5
3 Algeria 3
4 Andorra 50.3
5 Angola 35.4
6 Antigua and Barbuda 162
7 Argentina 74.9
8 Armenia 77.3
9 Australia 85.7
10 Austria 105.
# … with 179 more rows
%>% group_by(Country, Year)%>% summarise(Litres = sum(Litres, na.rm = TRUE))
How has the popularity of each type of alcohol changed over the years for different countries and have the local favorites of earlier decades been overtaken by other types of alcohol in terms of consumption?
>>>>>>> Added the map of beer consumptionCalculation Method:
Cancer Death \(\times\) Alcohol-Attributed Cancer Death Rate
Liver Cirrhosis Death \(\times\) Alcohol-attributed Liver Cirrhosis Death Rate
Road Traffic Death \(\times\) Alcohol-attributed Road Traffic Death Rate
Sum up all types of alcohol-attributed deaths
Analysis:
Alcohol causes the highest death rate in Nigeria and the lowest in Lybia.
Alcohol brings the highest number of deaths in African countries while the lowest in Middle East countries.
Alcohol causes more deaths to Males.
Liver Cirrhosis causes the highest death rate while Road Traffic Accidents causes the lowest.
Possible reasons affecting death rates
Religions
Income
Education
| income group | alcohol-attribute death | road traffic death |
|---|---|---|
| low | 28.25030 | 44.315714 |
| upper_middle | 24.83446 | 20.202222 |
| lower_middle | 24.16239 | 25.591026 |
| high | 18.70564 | 9.362195 |
Harms of alcohol
Cancer
Liver Cirrhosis
Road traffic accidents
Calculation method:
Analysis:
Alcohol causes more deaths to Males.
Liver Cirrhosis causes the highest death rate while Road Traffic Accidents causes the lowest.
| Action Area | mean |
|---|---|
| Reducing the negative consequences of drinking and alcohol intoxication | -0.1967742 |
| Monitoring and surveillance | -0.1297297 |
| Pricing policies | -0.1004082 |
| Reducing the public health impact of illicit alcohol and informally produced alcohol | -0.0850000 |
| Availability of alcohol | -0.0640541 |
| Drink-driving policies and countermeasures | -0.0428125 |
| Health services’ response | -0.0420290 |
| Community and workplace action | -0.0132000 |
| Marketing of alcoholic beverages | -0.0072727 |
| Leadership, awareness and commitment | 0.0222872 |
The biggest dip in consumption was seen in Mongolia in the year 2013 after the step - International alcohol study..
The biggest spike in consumption was seen in Spain in the year 2014 after the step - Si estás embarazada, con el alcohol no hay excusas [If you are pregrant, there is no excuse for alcohol]. Menores ni una gota de alcohol [Minors, not a drop of alcohol]. Media campaigns aimed at both minors and pregnant women implemented by Ministerio de Sanidad [Ministry of Health] and Federación Española de Bebidas Espirituosas [Spanish Federation of Spirit-based Beverages].
GHO | global information system on alcohol and Health (gisah) | global information system on alcohol and health. (n.d.). Retrieved May 09, 2021, from https://apps.who.int/gho/data/node.gisah.GISAH?lang=en&showonly=GISAH
---
title: "Analysis of Alcohol Consumption Patterns"
output:
flexdashboard::flex_dashboard:
vertical_layout: scroll
theme: bootstrap
storyboard: true
source_code: embed
navbar:
- { title: "Hayaku", align: right}
- { title: "Kexin Xu, Smriti Vinayak Bhat, Yalong liu, Yin Shan Ho", align: right}
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(knitr)
library(tidytuesdayR)
library(tidyverse)
library(sf)
library(rnaturalearth)
library(gganimate)
library(viridis)
library(RColorBrewer)
library(rgeos)
library(gifski)
```
Introduction {.tabset}
======================
Column {.tabset}
-------------------------------------
### Background
Column {.tabset} {data-width=100}
-------------------------------------
### Approach of Project
The report will be collating specific sections of this data pertaining to countrywise policy responses, consumption pattern, youth and alcohol, levels of consumption and harms and consequences.
### Information on Data
This report is being created using the data from the GISAH (Global Information System on Alcohol and Health) tool of the World Health Organisation. GISAH tool helps monitor trends relating to alcohol consumption across various age groups, side effects (harm caused) as well as the policy responses by different countries.
Popularity {.tabset}
======================
Column {data-width=400}
-------------------------------------
### Total Consumption
```{r}
consumption <- read_csv("../clean_data/consumption_join.csv")
```
```{r}
# Origin data pivot longer
b_tidy <- consumption %>%
filter(Beverage_Types == "Spirits") %>%
pivot_longer(cols = -c(Country, Beverage_Types),
names_to = "Year",
values_to = "Litres")
b_tidy
```
```{r}
# Loading countries data
countries <- ne_countries(returnclass = "sf", scale = "medium") %>%
select(admin, continent, pop_est, gdp_md_est, economy, income_grp )
```
```{r}
# Test unmatched countries
anti_join(b_tidy, countries, by = c("Country" = "admin"))
```
```{r}
# Modify the country name consisting with rnaturalearth package
b_l_country <- b_tidy %>%
mutate(Country = recode(Country,
"Bahamas" = "The Bahamas",
"Bolivia (Plurinational State of)" = "Bolivia",
"Brunei Darussalam" = "Brunei",
"Cabo Verde" = "Cape Verde",
"Congo" = "Democratic Republic of the Congo",
"Côte d'Ivoire" = "Ivory Coast",
"Czechia" = "Czech Republic",
"Democratic People's Republic of Korea" = "North Korea",
"Eswatini" = "Swaziland",
"Guinea-Bissau" = "Guinea Bissau",
"Iran (Islamic Republic of)" = "Iran",
"Lao People's Democratic Republic" = "Laos",
"Micronesia (Federated States of)" = "Federated States of Micronesia",
"North Macedonia" = "Macedonia",
"Republic of Korea" = "South Korea",
"Republic of Moldova" = "Moldova",
"Russian Federation" = "Russia",
"Serbia" = "Republic of Serbia",
"Syrian Arab Republic" = "Syria",
"Timor-Leste" = "East Timor",
"Tuvalu" = "Wallis and Futuna",
"United Kingdom of Great Britain and Northern Ireland" = "United Kingdom",
"Venezuela (Bolivarian Republic of)" = "Venezuela",
"Viet Nam" = "Vietnam"))%>%
filter(!is.na(Country))
```
```{r}
# Beer popularity by time
b_l_country_time <- left_join(countries, b_l_country, by = c("admin" = "Country"))
b_time <- ggplot()+
geom_sf(data = countries, fill = NA)+
geom_sf(data = b_l_country_time %>% filter(!is.na(Year)), mapping = aes(fill = Litres))+
scale_fill_viridis(na.value="white")+
labs(title = "Countries where Beer is popular",
subtitle = "Year: {current_frame}",
fill = "Total")+
theme_bw()+
transition_manual(Year)
animate(b_time, duration = 30, fps = 20, width = 1000, height = 500, renderer = gifski_renderer())
```
### Total Percentage
*pie*
```{r}
consumption %>%
select(Beverage_Types, "2018") %>%
group_by(Beverage_Types) %>%
summarise(no = sum(`2018`, na.rm = TRUE))
```
```{r}
# The number of litres consumed for each country
b_l_country <- b_tidy %>%
group_by(Country)%>%
summarise(Litres = sum(Litres, na.rm = TRUE))
b_l_country
```
%>%
group_by(Country, Year)%>%
summarise(Litres = sum(Litres, na.rm = TRUE))
### Research Question 1
**How has the popularity of each type of alcohol changed over the years for different countries and have the local favorites of earlier decades been overtaken by other types of alcohol in terms of consumption?**
### Research Question 2
**Does alcohol take more lives in traffic crashes or illnesses and which countries suffer more from alcohol-related cases?**
### Research Question 3
**What are the recurring patterns of policy responses across different countries in the world and how has it impacted the consumption of alcohol?**
### Research Question 4
**Do drinking age restrictions and legal blood alcohol concentration (BAC) limits for young drivers have any effect on adolescents’ drinking among countries?**
### Research Question 5
**What are the trends in youth drinking issues over the years?**
Findings {.tabset}
======================
Limitations {.tabset}
======================
Conclusion {.tabset}
======================
References {.tabset}
======================
GHO | global information system on alcohol and Health (gisah) | global information system on alcohol and health. (n.d.). Retrieved May 09, 2021, from https://apps.who.int/gho/data/node.gisah.GISAH?lang=en&showonly=GISAH